Improving Familial Hypercholesterolemia Diagnosis Using an EMR-based Hybrid Diagnostic Model

CONTEXT: Familial hypercholesterolemia (FH) confers a greatly increased risk for premature cardiovascular disease, but remains very underdiagnosed and undertreated in primary care populations. OBJECTIVE: We assessed whether using a hybrid model consisting of 2 existing FH diagnostic criteria coupled with electronic medical record (EMR) data would accurately identify patients with FH in a Midwest US metropolitan healthcare system. METHODS: We conducted a retrospective, records-based, cross-sectional study using datasets from unique EMRs of living patients. Using Structured Query Language to identify components of 2 currently approved FH diagnostic criteria, we created a hybrid model to identify individuals with FH. RESULTS: Of 264 264 records analyzed, between 794 and 1571 patients were identified as having FH based on the hybrid diagnostic model, with a prevalence of 1:300 to 1:160. These patients had a higher prevalence of premature coronary artery disease (CAD) (38-58%) than the general population (1.8%) and higher than those having a high CAD risk but no FH (10%). Although most patients were receiving lipid-lowering therapies (LLTs), only 50% were receiving guideline-recommended high-intensity LLT. CONCLUSION: Using the hybrid model, we identified FH with a higher clinical and genetic detection rate than using standard diagnostic criteria individually. Statin and other LLT use were suboptimal and below guideline recommendations. Because FH underdiagnosis and undertreatment are due partially to the challenges of implementing existing diagnostic criteria in a primary care setting, this hybrid model potentially can improve FH diagnosis and subsequent early access to appropriate treatment.